code: def test_score_function(daily_data):
      """Checks score function accuracy"""
      # with null model
      model = AutoArimaEstimator(null_model_params={"strategy": "mean"})
      train_df = daily_data["train_df"]
      value_col = "y"
      time_col = "ts"
      model.fit(train_df, time_col=time_col, value_col=value_col)
      score = model.score(
          daily_data["test_df"], daily_data["test_df"][value_col]
      )
      assert score < 0.40
  
      # without null model
      model = AutoArimaEstimator()
      train_df = daily_data["train_df"]
      value_col = "y"
      time_col = "ts"
      model.fit(train_df, time_col=time_col, value_col=value_col)
      score = model.score(
          daily_data["test_df"], daily_data["test_df"][value_col]
      )
      assert score < 9.0
  
imports: import numpy
  import pandas
  import pytest
  from pmdarima.arima import AutoARIMA
  from sklearn.metrics import mean_absolute_error
  from sklearn.metrics import mean_squared_error
  from testfixtures import LogCapture
  from greykite.common.constants import LOGGER_NAME
  from greykite.common.constants import PREDICTED_COL
  from greykite.common.constants import PREDICTED_LOWER_COL
  from greykite.common.constants import PREDICTED_UPPER_COL
  from greykite.common.constants import TIME_COL
  from greykite.common.constants import VALUE_COL
  from greykite.common.evaluation import EvaluationMetricEnum
  from greykite.common.evaluation import calc_pred_err
  from greykite.common.logging import LoggingLevelEnum
  from greykite.common.python_utils import assert_equal
  from greykite.common.testing_utils import generate_df_for_tests
  from greykite.sklearn.estimator.auto_arima_estimator import AutoArimaEstimator
label_desc: Use image node_id 7 for calling a global function with example usage: test_score_function(daily_data) without return types